1 Title
When Adolescents Receive Sexual Messages on the Internet: Explaining Experiences of Risk and Harm
Sonia Livingstone1 & Anke Görzig1
1London School of Economics and Political Science Author Note
This article draws on the work of the ‘EU Kids Online’ network funded by the EC (DG Information Society) Safer Internet Programme (project code SIP-KEP-321803). Correspondence may be sent to Prof Sonia Livingstone, Department of Media and Communications, Houghton Street, London, WC2A 2AE, UK (email:
2
Abstract
This article reports new findings on the incidence of risk and the associated experience of harm reported by children and adolescents aged 11-16, regarding receipt of sexual messages on the internet (known popularly as sexting). Findings showed that the main predictors of the risk of seeing or receiving sexual messages online are age (older), psychological difficulties (higher), sensation seeking (higher) and risky online and offline behaviour (higher). By contrast, the main predictors of harm resulting from receiving such messages were age (younger), gender (girls), psychological difficulties (higher) and sensation seeking (lower), with no effect for risky online or offline behaviour. The findings suggest that accounts of internet-related risks should distinguish between predictors of risk and harm. Since some exposure to risk is necessary to build resilience, rather than aiming to reduce risk through policy and practical interventions, the findings can be used to more precisely target those who experience harm in order to reduce harm overall from internet use..
Keywords
3
When Adolescents Receive Sexual Messages on the Internet:
Explaining Experiences of Risk and Harm
1. Introduction
Public, policy and research attention has recently been paid to the peer-to-peer exchange of sexual messages using digital technologies (known popularly as sexting). Such messages may be created and exchanged via text or image messaging on mobile phones, though they also include peer-to-peer messaging on diverse internet-enabled devices, particularly using social networking sites and instant messaging services. Although in some respects now part of the fun, flirtation and identity-experimentation central to teenage culture (Buckingham & Bragg, 2004; Hope, 2007; Ringrose, Gill, Livingstone, & Harvey, 2012; Willett & Burn, 2005), this exchange of sexual messages is attracting considerable public anxiety, amplified by the often exaggerated media coverage of particular cases (Draper, 2012; Haddon & Stald, 2009). This anxiety arises partly because of aggressive or coercive nature of some messages (for links with sexual harassment, see Burgess-Proctor, Patchin, & Hinduja, 2009; Salter, Crofts, & Lee, 2013; for links with grooming, see Palmer & Stacey, 2004), and partly even if voluntary, some images involved are sufficiently explicit as to be potentially illegal (Albury, Crawford, Byron, & Mathews, 2013; Arcabascio, 2010; Sacco, Argudin, Maguire, & Tallon, 2010; Willard, 2010).
4
adopting digital communication technologies rapidly, often far ahead of the adults charged with their safety and wellbeing.
Thus far, researchers have struggled to agree on matters of definition and measurement, although this is vital if research is to produce robust evidence regarding prevalence, distribution and consequences (Lounsbury, Mitchell, & Finkelhor, 2011). In consequence, survey findings vary widely, ranging from a reported 7% (Mitchell, Finkelhor, Jones & Wolak, 2012) to 15% (Lenhart, 2009) to as many as 48% (National Campaign to Support Teen and Unplanned Pregnancy, 2008). Qualitative research adds further complications. Some studies find that adolescents’ own accounts emphasise the willing exchange of messages between romantic partners, typically involving self-generated images (Lenhart, 2009). They recognise that some adolescents who send and receive sexual messages find it fun or flirtatious, and it may even be seen as a form of creative media production (Hasinoff, 2012). Others distinguish primary from secondary sexting, arguing that different contexts apply to the voluntary creation and sending of a sexual image between partners and the subsequent circulation of such an image beyond the control of its creator (Lievens, 2012). Yet others recognise that there can be different forms of sexting, some harmful and some not (or, as Wolak & Finkelhor, 2011, term it, aggravated and experimental).
5
Daneback, & Kvapilik, 2012). Some who create and send sexual images feel pressured to do so (National Campaign to Support Teen and Unplanned Pregnancy, 2008) or are upset either on receiving such messages or when sexual messages they have created are circulated beyond the intended recipient (Phippen, 2012).
Consequently, it is helpful to differentiate the prevalence of sexual messaging (which, as suggested by surveys, encompasses a sizable minority of adolescents) from reported responses (which, qualitative research suggests, are negative only for a further subset of those who see or receive such messages). To understand this distinction better, we draw on theories of risk (Aven & Renn, 2009; Breakwell, 2010) to distinguish risk (defined as the occurrence of an event which is associated with a probability of harm) from harm (defined as actual physical or mental damage as reported by the person concerned).
At present, awareness-raising initiatives tend to address all children and young people, creating the perception of a widespread problem. Possibly it would help diffuse public anxiety if research could resolve the uncertainty regarding which adolescents are likely to encounter sexual messages or to be harmed as a result, enabling better targeting of safety initiatives to focus on the minority particularly at risk of actual harm. To progress this, we observe that although the internet-enabled technologies are a relatively recent addition to adolescents’ lives, much is already known regarding their
6
distinct effects – on the likelihood of encountering risk and/or on the likelihood that a risk encounter is experienced as harmful.
2. Explaining (Online) Risk and Harm
Adolescence is characterised by the tension between dependence and independence. Adolescents are motivated to assert their desires and exercise their abilities, both means of building resilience, but this faces them with a host of personal, relational and educational demands that test their competences and reveal their
continuing need for support. Psychological, social and economic advantage or disadvantage is particularly likely to impact on wellbeing and life chances during adolescence, with some young people experiencing more risks than others, and with one form of disadvantage tending to compound another (Currie et al, 2008; Feinstein & Sabates, 2006; Schoon, 2006). Adolescence is also the life stage in which young people experiment with identity and sexuality, including testing themselves against the adult-created boundaries designed to keep them safe (Coleman & Hagell, 2007).
Characterised as a period of increased risk-taking (e.g., Burke et al., 1997; van Nieuwenhuijzen et al., 2009), this too contributes to vicious or virtuous circles, with those engaging in one type of risk behaviour being more likely to engage in others (e.g., Guilamo-Ramos, Litardo, & Jaccard, 2005; Rice et al., 2012).
Recent approaches to the analysis of adolescent risk propose moving away from interventions designed for specific risk behaviours to embrace a more integrative approach focused on risk behaviours in general (Hale & Viner, 2012; Jackson,
7
engage in any one type of risk behaviour will enhance the propensity to risks in general (Donovan & Jessor, 1985; Jessor, 1991). An influential personality factor associated with adolescent risk behaviour is sensation seeking. Defined as the dispositional tendency to seek out new experiences, this is linked to a lack of inhibition and an attitude of risk-taking (Stephenson, Hoyle, Palmgreen, & Slater, 2003; Zuckerman, 1979, 1994). The motivational inclination of sensation seekers to look for new
opportunities is associated with puberty-specific, maturational changes including sexual interest and emotional intensity (Steinberg et al., 2008). Sensation seekers find risky experiences more pleasurable and, in turn, this may contribute to resilience; conversely, those lower in sensation seeking are less likely to seek sensation-enhancing experiences and may be more easily upset when they encounter them (Farmer et al., 2001; Smith et al., 1992).
8
psychological difficulties may not only predict risk but also vulnerability to harm in consequence.
In terms of behavioural factors predicting risk online, the nature of the internet itself adds a further complication. The interactional distance it inserts between people, the ambiguity of its social norms, and the promise of exciting new opportunities online have combined to support risky youthful practices such as making one’s personal information public, looking for new contacts online, or pretending to be a different kind of person online (Livingstone, 2008; Baumgartner, Valkenburg, & Peter, 2010a).
Supporting the idea of a common factor underlying various kinds of adolescents’ risk behaviours, it is generally assumed (although little demonstrated) that those who encounter risks offline are more likely to encounter risks online. This focus on propensity to risk recognises the influence of personality (psychological difficulties, sensation seeking) and behavioural (risk-taking) factors which apply across domains, including across the offline/online boundary. Qualitative research has already shown that this boundary is much less salient to youth than to adults (boyd, 2008; Orgad, 2007). The hypothesis that those who encounter offline risks are more likely to
encounter online risks, whether because of their personality or behaviour, is supported by survey evidence (e.g., Palfrey, Sacco, boyd, & DeBonis, 2008; Wolak, Finkelhor, & Mitchell, 2008), clinical reports (Delmonico & Griffin, 2008; Mitchell & Wells, 2007; Palmer & Stacey, 2004), policy analysis (Byron, 2008) and criminal cases (CEOP, 2010).
9
psychological difficulties are both more likely to encounter risk online and also to be more vulnerable to harm associated with that risk (e.g. Wells & Mitchell, 2008; Wolak, Finkelhor & Mitchell, 2008). On the other hand, supporting the idea that risk and harm require different explanations is the finding that, while sexual messaging is received by girls and boys equally, it is experienced more often as harassment by girls than by boys (Ringrose et al, 2012; Ybarra & Mitchell, 2008). Moreover, when risk is encountered as a result of greater sensation-seeking, that same orientation which leads to an increase in risk (Brady & Donenberg, 2006; Dowell, Burgess, & Cavanaugh, 2009; Slater, 2003; Slater, Henry, Swaim, & Cardador, 2004) may also enable adolescents to build
resilience, thus reducing the likelihood of harm (Hasebrink, Görzig, Haddon, Kalmus, & Livingstone, 2011; Livingstone, Haddon, & Görzig, 2012; Valkenburg & Peter, 2008).
3. Hypothesises regarding risk and harm associated with receiving sexual
messages
Although the literature on online risks is growing, there is still insufficient basis to ground detailed hypotheses on the basis of an agreed theoretical framework.
However, the above discussion offers a tentative ground to formulate some hypotheses regarding the possible influence of psychological difficulties and sensation seeking which are consistent with the literature on adolescent risk as well as emerging findings on internet use. Additionally, the claim of a common risk propensity leads us to
10
the exchange of sexual messages is more common among older than younger
adolescents and more problematic for girls (Baumgartner, Valkenburg & Peter, 2010b; Ybarra & Mitchell, 2008), and because of findings linking risk-taking to boys (Brady & Donenberg, 2006; Byrnes, Miller, & Schafer, 1999).
It seems likely also that the nature of adolescent internet use matters, since young people who engage in more online activities encounter more risks as well as more opportunities (Livingstone & Helsper, 2010), possibly because they search more widely or have developed more digital skills. Although there is no easy line to be drawn between generally risky activities and those that carry a specific risk of harm, it may be that those who practise risky online activities (including identity and communication-related activities for which established norms of conduct and safety are undeveloped) also encounter more online risks. If so, as for sensation seeking or, indeed, risky offline activities, this increased exposure to risk may result in resilience rather than harm. Although such a hypothesis must remain tentative, we considered this worthy of
investigation, partly because providing guidance on risky online activities has been and could yet be the focus of awareness-raising and safety guidance.
As argued above, it is important to examine the influence of these factors on both risk (here, operationalized as receiving sexual messages online) and harm (operationalized as being upset by such messages). It is a strength of our study that, having measured these separately, we can examine the influence of the same factors on both. Thus we anticipate that higher psychological difficulties will be associated with higher risk and more harm, and that offline risk-taking, online risky activities and sensation seeking will be linked to more risk but less harm (insofar as repeated
11
of age, gender and internet use (operationalized as range of online activities) in order to control for and explore their effects before examining the variables of interest.
Acknowledging the limited evidence base on which to ground our analysis, we hypothesise as follows:
H1: Adolescents are more likely to receive sexual messages online when they:
Are higher in psychological difficulties
Are higher in sensation seeking
Engage more in risky offline activities
Engage more in risky online activities
H2: Adolescents are more likely to be upset by receiving sexual messages online when they:
Are higher in psychological difficulties
Are lower in sensation seeking
Engage less in risky online activities
Engage less in risky offline activities
4. Method
4.1Participants and Procedure
12
interviewer nor parent could see their answers, depending on the technology available to fieldworks in different countries. Questions about sexual messages were posed only to 11-16 year olds, with a core sample size of 18 709 (50% girls/boys). For full details of sampling and procedures, see Livingstone, Haddon, Görzig, & Ólafsson (2011) and Görzig (2012).
4.2Dependent Measures
4.2.1 Risk (receiving sexual messages online)
Respondents received the following introduction: “[People] may send sexual messages or images. By this, we mean talk about having sex or images of people naked or having sex... In the past 12 months, have you seen or received sexual messages of any kind on the internet?” The risk measure of receiving sexual messages was coded “1” for those who responded “yes” and “0” for those who had responded “no”. Non-respondents (answering “don’t know” or “prefer not to say” to any measure) were excluded from the analyses resulting in a sample size of n=15 619.1
Fifteen per cent (n=2 214) of 11-16 year olds indicated that they had received sexual messages on the internet.2
4.2.2 Harm (from receiving sexual messages)
13
The measure for harm from receiving sexual messages was coded “1” for those who responded “yes” and “0” for those who had responded “no”. Missing values were excluded from the analyses resulting in a sample size of n=2 036.1 Twenty-four per cent (n=519) of those who had experienced sexual messaging online indicated that this experience had upset them.
4.3 Independent Measures
4.3.1 Demographic and psychological factors
Demographic variables were age (11-16 years) and gender (50% girls). We employed two psychological measures which showed good internal consistency for the EU Kids Online sample as a whole. (1) Sensation seeking, a two-item version of the Sensation Seeking Scale-Form V (SSS-V; Stephenson, Hoyle, Palmgreen, & Slater, 2003): 2 items, r = .64, p < .001. (2) Psychological difficulties, adapted from
Goodman’s (1998) SDQ using items measuring psychological difficulties only: 16 items, α = .71; for scale properties of the adapted psychological scales, see Livingstone, Haddon, & Görzig, 2012). Items for sensation seeking and psychological difficulties were measured on a scale from 1 (not true for me) to 3 (very true for me) and averaged within the sample of 11-16 year olds (N=18 709), (sensation seeking: M = 1.39; SD = .53; psychological difficulties: M = 1.40, SD = .25).
4.3.2 Online activities
14
networking profile”, “Used instant messaging”, “Sent/received email”, “Read/watched the news on the internet”, “Played games with other people on the internet”,
“Downloaded music or films”, “Put (or posted) photos, videos or music to share with others”, “Used a webcam”, “Put (or posted) a message on a website”, “Visited a
chatroom”, “Used file sharing sites”, “Created a character, pet or avatar”, “Spent time in a virtual world”, “Written a blog or online diary” (α = .76; M = 8.13; SD = 3.47; N=18 709).
4.3.3 Risky online activities
Respondents were asked about behaviour in the previous month, based on the number out of five options (adapted from Livingstone & Helsper, 2010): “Looked for new friends on the internet”, “Added people to my friends list or address book that I have never met face-to-face”, “Pretended to be a different kind of person on the internet from what I really am”, “Sent personal information to someone that I have never met to-face”, “Sent a photo or video of myself to someone that I have never met face-to-face” (α = .72; M = 1.29; SD = 1.42; N=18 709).
4.3.4 Risky offline activities
15
5. Results
Separate analyses using identical procedures and predictor variables were conducted to test the hypotheses regarding risk and harm associated with receiving sexual messages online.
5.1 Risk of Receiving Sexual Messages
A multi-level hierarchical logistic regression analysis was performed for internet-using 11-16 year olds (N=15 619). Although country differences were not part of our theoretical model, we performed multi-level modelling analyses to control for country differences and account for the equal sample sizes between countries despite unequal population sizes. As a first step, a model with no predictors (the null-model) was conducted to assess variation in the odds of receiving sexual messages online across countries. The variation between countries was significant (χ2(1)=170.43,
p<0.001), but only 4% of the variation in the odds of receiving sexual messages online was attributable to between-country differences (variance partitioning coefficient (VPC); cf. Goldstein, Browne, & Rasbash, 2002). The odds of receiving sexual messages online ranged from lowest in Italy and Ireland (Exp(B)’s = 0.05 and 0.10) to
highest in Estonia and Romania (Exp(B)’s = 0.25 and 0.28).
Table 1 about here
16
(Exp(B) = 1.27; p < .001). Psychological factors were entered in Model 3 to determine their effect over and above the effects of age and gender. Higher sensation seeking was associated with more than two-fold greater likelihood of receiving sexual messages per scale point (Exp(B) = 2.21; p < .001), with a similar effect showing for psychological difficulties (Exp(B) = 2.69; p < .001) and these effects hold independently of age and gender while reducing the effect of gender slightly below statistical significance (Exp(B) = 1.10; p = .051). In Model 4 we entered variables associated with risky behaviours, i.e., risky online activities and risky offline activities, while controlling for engagement in online activities in general. Risky online activities (Exp(B) = 1.44) and risky offline activities (Exp(B) = 1.49) were both associated with an almost 50% increase in the likelihood of receiving sexual messages per single activity. Online activities were also but less strongly associated with receiving sexual messages (Exp(B)
= 1.13) , all (p’s < .001), further reducing the effect of gender to null (Exp(B) = 1.01; p
= .812) and considerably reducing the effects of age (Exp(B) = 1.20; p < .001), sensation seeking (Exp(B) = 1.34; p < .001) and psychological difficulties (Exp(B) = 1.54; p < .001) (i.e., by almost half). The significant Log-likelihood ratios (all p’s < .001) show
that each model improved the fit compared with the previous model.
5.2Harm from Receiving Sexual Messages
A multi-level hierarchical logistic regression analysis was performed for internet-using 11-16 year olds who indicated to have received a sexual message on the internet in the past year (N=2 036). The model with no predictors (the null-model), showed that the variation in the odds of harm from receiving sexual messages was significant between countries (χ2
(1)=52.81, p<0.001): 8% of this variation was
17
2002).The odds of harm from receiving sexual messages online ranged from lowest in Finland and Slovenia (Exp(B)’s = 0.06 and 0.14) to highest in Romania and Turkey
(Exp(B)’s = 0.69 and 0.73).
Table 2 about here
Similar to the analyses for risk, predictor variables were entered in three sequential models (Model 2, 3 and 4; see Table 2). Model 2 showed that harm from sexual messaging is associated with younger children, a likelihood increase of 14% per year (Exp(B) = 0.76; p < .001) and girls, 60% more likely than for boys,(Exp(B) = 0.40; p < .001). Model 3 showed that higher sensation seeking is associated with less harm, a 16% decrease per scale point (Exp(B) = 0.74; p < .001), while more psychological difficulties are strongly associated with more harm, an almost four-fold increase per scale point (Exp(B) = 3.82; p < .001). Both effects occurred over and above the effects of the demographic variables (the effects of the latter variables decreased very little when psychological factors were added to the model). In Model 4, the behavioural variables were statistically insignificant although the variable controlling for usage showed a significant but small negative effect, a decrease of 5% per activity (Exp(B) = 0.95; p < .01). Each of the four models in the hierarchical regression improved the fit compared to the previous model significantly (all p’s < .05).
6. Discussion
18
unsafe behaviour. With the goal of producing evidence useful to policy making, this article has addressed two pressing concerns. First, it has distinguished the incidence of risk (here, the receipt of sexual messages online) from the incidence of harm (here, reported upset from receiving such messages). Second, it has examined which factors predict risk and which factors predict harm in order to pinpoint which adolescents are particularly at risk of harm. Building on prior theories of risk and protective factors operating in adolescence generally, particularly as these may undermine or contribute to resilience, we tested contrasting hypotheses that could account for risk and harm within a large sample of 11-16 year old internet users.
The risk of receiving a range of sexual messages increases with age from 11 to 16 years, as expected from prior research, but the finding for gender (slightly more boys encountering the risk of receiving sexual messages) qualifies prior findings of no
notable gender differences. As predicted, risk was greater among those higher in sensation seeking and in psychological difficulties. How adolescents behave on and offline also makes a difference: those engaging in more risky offline and online activities (controlling for internet usage in general) were more likely to receive sexual messages online. Adding the behavioural variables reduced the effect of the
19
our initial notion of a common factor underlying various kinds of adolescents’ risk behaviours which might affect the occurrence of one particular risk (here sexual messaging).
Interestingly, albeit in line with prior research the demographic findings for harm are the inverse of those for risk. While receiving sexual messages is more common as adolescents get older, and among boys, when this risk is encountered by younger adolescents and girls, they are more upset by it. The explanation for harm also differs from that found for risk. As predicted, those with higher psychological
difficulties experience more harm; those with higher sensation seeking less, supporting the claim that a degree of sensation seeking permits adolescents to cope with risk, thereby building resilience to harm. Other than a slight effect for internet usage (more usage, less harm), again supporting the resilience claim, the behavioural variables had no effect on harm, contra the prediction advanced earlier. Thus, among those who receive sexual messages, whether or not it upsets them depends mainly on their age and gender as well as their psychological make-up, and is largely unaffected by their level of online or offline risky behaviours – even though, as already noted, this is the most important factor in explaining risk.
Policy makers, parents, industry and child welfare professionals face several difficulties in determining how to respond to the online exchange of sexual messages among adolescents. As noted in the introduction to this article, these include matters of definition and legality, as well and the challenge of determining whether such
20
factor for the experience of harm, strategies designed to reduce harm must attend to the conditions that sustain risk.
As we have seen, the incidence of risk across the population is fairly small, and the incidence of harm is even smaller, making it a costly and, potentially, inefficient use of resources to target safety initiatives at the entire youth population. The findings in this article suggest that older compared to younger adolescents receive a greater range of sexual messages, both because their practices of internet use are more diverse and because they encounter or seek out more risks - online and offline. But for most adolescents, the consequences are unproblematic, possibly enjoyable. So, while
awareness raising efforts should continue to address the sending and receiving of sexual messages among adolescents, these should recognise the cultural complexities of
emerging cultural, sexual and social media norms and practices within the peer group (see Baumgartner, Sumter, Peter, & Valkenburg, 2012; Mitchell, Finkelhor, Jones, & Wolak, 2012). Moreover, given today’s risk averse culture (Gill, 2007), it is useful to note that, while sensation seeking increases risk, it does not increase harm, even reducing it somewhat.
21
voluntary and coercive practices, and between ‘sexting’ and bullying, with practical interventions embedded in sexual and health education – including sometimes in gender-segregated sessions - rather as part of computing or technology classes. Insofar as younger children report more harm than older adolescents, this raises particular challenges for awareness raisers and educators who are reluctant to raise sexual matters with young children: it may be that such work should be conducted in tandem with parents, as well as embedding messages of sexual rights and respect in education even for young children. Finally, since harm is more often reported by those who face psychological difficulties, the challenge is here that this group already tends to need more social, parental and psychological support than it may receive. Although the finding of a negative association between adolescents’ range of online activities and experience of harm was small, there may be scope to work with vulnerable children to extend their range of online activities, thereby building their online confidence and resilience and so potentially reducing future harm by providing them with more coping strategies.
The study reported in this article was novel in its effort to uncover the potentially different conditions that explain risk and harm. However, it has several limitations and more research is needed to guide future policy initiatives. First, there must be further factors yet to be examined that may account for risk and harm, not captured in the EU Kids Online survey: possibilities include adolescents’ level of sexual maturity (as distinct from their age), their parental values and norms, and practices of
communication within specific peer groups or subcultures (Brown, Keller, & Stern, 2009). Further, since it is possible that adolescents who are older, with more
22
receiving sexual messages, the results of this study should be triangulated with results that do not rely on self-reported effects of sexual messaging. These might derive from direct examination of adolescent message exchanged online, for instance. Qualitative methods, too, may offer a deeper understanding about the circumstances in which such messages are exchanged, possibly as part of a sexualised school environment (Ringrose et al, 2012), long-established sexual double standards in the culture (Albury et al., 2013) or as part of the drama of peer culture (Marwick & boyd, 2011).
Last, we observe that we have examined the risk of harm to children and young people online in relation to the receipt of sexual messages, as is common in this field (where researchers tend to examine the nature and consequences of sexual messages, or pornography, or cyberbullying with each analysis embedded in its own research
23
References
Albury, K., Crawford, K., Byron, P., & Mathews, B. (2013). Young people and sexting in Australia: Ethics, representation and the law. ARC Centre for Creative
Industries and Innovation/ Journalism and Media Research Centre, The University of New South Wales, Australia.
Arcabascio, C. (2010). Sexting and teenagers: OMG R U GOING 2 JAIL??? Richmond Journal of Law & Technology, 16(3), 1-43.
Aven, T., & Renn, O. (2009). On risk defined as an event where the outcome is uncertain. Journal of Risk Research, 12(1), 1-11.
Baumgartner, S., Sumter, S., Peter, J., & Valkenburg, P. (2012). Identifying Teens at Risk: Developmental Pathways of Online and Offline Sexual Risk Behavior,
Pediatrics, doi:10.1542/peds.2012-0842
Baumgartner, S., Valkenburg, P. M., & Peter, J. (2010a). Assessing causality in the relationship between adolescents' risky sexual online behavior and their perceptions of this behavior. Journal of Youth Adolescence, 39, 1226-1239. doi:10.1007/s10964-010-9512-y
Baumgartner, S., Valkenburg, P. M., & Peter, J. (2010b). Unwanted online sexual solicitation and risky sexual online behavior across the lifespan. Journal of Applied Developmental Psychology, 31, 439-447. doi:10.1016 /j.appdev.2010.07.005 boyd, d. (2008). Why youth ♥ social network sites: The role of networked publics in
teenage social life. In D. Buckingham (Ed.), Youth, Identity, and Digital Media
(Vol. 6, pp. 119–142). Cambridge, MA: MIT Press.
Brady, S. S., & Donenberg, G. R. (2006). Mechanisms linking violence exposure to health risk behavior in adolescence: Motivation to cope and sensation seeking.
Journal of American Academy of Child and Adolescent Psychiatry, 45(6), 673-680. Breakwell, G. M. (2010). The psychology of risk. Cambridge, UK: Cambridge
University Press.
Brown, J. D., Keller, S., & Stern, S. (2009). Sex, sexuality, sexting and sexed: Adolescents and the media. The Prevention Researcher, 16(4), 12-16.
Buckingham, D., & Bragg, S. (2004). Young people, sex and the media: The facts of life? Basingstoke, UK: Palgrave Macmillan.
Burgess-Proctor, A., Patchin, J. W., & Hinduja, S. (2009). Cyberbullying and online harassment: Reconceptualizing the victimization of adolescent girls. In V. Garcia & J. Clifford (Eds.), Female crime victims: Reality reconsidered. Upper Saddle River, NJ: Prentice Hall.
24
Australians. Preventive medicine, 26(5 Pt 1), 724–33. doi:10.1006/pmed.1997.0198
Byrnes, J. P., Miller, D. C., & Schafer, W. D. (1999). Gender differences in risk taking: A meta-analysis. Psychological Bulletin, 125(3), 367-383.
Byron, T. (2008). Safer Children in a Digital World: The Report of the Byron Review. London: Department for Children, Schools and Families, and the Department for Culture, Media and Sport.
Child Exploitation and Online Protection Centre (CEOP) (2010). CEOP Annual Review 2009-2010. London, UK: Child Exploitation and Online Protection Centre.
Coleman, J., & Hagell, A. (Eds.). (2007). Adolescence, risk and resilience: Against the odds. Chichester, UK: Wiley.
Critcher, C. (2008). Making waves: Historic aspects of public debates about children and mass media. In K. Drotner & S. Livingstone (Eds.), International handbook of children, media and culture (pp. 91-104). London, UK: Sage.
Currie, C., Gabhainn, S, N., Godeau, E., Roberts, C., Smith, R., Currie, D., Picket, W., Richter, M., Morgan, A., & Barnekow, V. (Eds.). (2008). Inequalities in young people's health: Health behaviour in school-aged children (HBSC) international report from the 2005/2006 survey. Copenhagen, Denmark: WHO Regional Office for Europe.
Delmonico, D. L., & Griffin, E. J. (2008). Cybersex and the e-teen: What marriage and family therapists should know. Journal of Marital and Family Therapy, 34(4), 431-444. doi:10.1111/j.1752-0606.2008.00086.x
Donovan, J. E., & Jessor, R. (1985). Structure of problem behavior in adolescence and young adulthood. Journal of consulting and clinical psychology, 53(6), 890–904. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/4086689
Dowell, E. B., Burgess, A. W., & Cavanaugh, D. J. (2009). Clustering of internet risk behaviors in a middle school student population. Journal of School Health, 79(11), 547 - 553. doi: 10.1111/j.1746-1561.2009.00447.x
Draper, N. R. A. (2012). Is your teen at risk? Discourses of adolescent sexting in United States television news. Journal of Children and Media, 6(2), 221-236.
Enoch, M. A., Steer, C. D., Newman, T. K., Gibson, N. N., & Goldman, D. D. (2010). Early life stress, MAOA, and gene-environment interactions predict behavioral disinhibition in children. Genes, Brain & Behavior, 9(1), 65-74.
doi:10.1111/j.1601-183X.2009.00535.x
25
Feinstein, L., & Sabates, R. (2006). Predicting adult life outcomes from earlier signals: identifying those at risk. London: Prime Minister's Strategy Unit.
Gill, T. (2007). No fear: Growing up in a risk averse society. London: Calouste Gulbenkian Foundation.
Goldstein, H., Browne, W., & Rasbash, J. (2002). Partitioning Variation in Multilevel Models. Understanding Statistics, 1(4), 223–231.
doi:10.1207/S15328031US0104_02
Goodman, R., Meltzer, H., Bailey, V. (1998). The strengths and difficulties
questionnaire: A pilot study on the validity of the self-report version. European Child and Adolescent Psychiatry, 7, 125-130. doi:10.1007/s007870050057
Goodman, R. R., Ford, T. T., Simmons, H. H., Gatward, R. R., & Meltzer, H. H. (2003). Using the strengths and difficulties questionnaire (SDQ) to screen for child
psychiatric disorders in a community sample. International Review of Psychiatry, 15(1-2), 166-172. doi:10.1080/0954026021000046128
Görzig, A. (2012). Methodological framework: the EU Kids Online project. In S. Livingstone, L. Haddon, and A. Görzig (Eds.), Children, risk and safety online: Research and policy challenges in comparative perspective (pp. 15-32). Bristol: The Policy Press.
Guilamo-Ramos, V., Litardo, H. a, & Jaccard, J. (2005). Prevention programs for reducing adolescent problem behaviors: Implications of the co-occurrence of problem behaviors in adolescence. The Journal of adolescent health : official
publication of the Society for Adolescent Medicine, 36(1), 82–6. doi:10.1016/j.jadohealth.2003.12.013
Haddon, L. & Stald, G. (2009). A comparative analysis of European press coverage of children and the internet. Journal of Children and Media, 3(4), 379-393,
doi:10.1080/17482790903233432
Hale, D. R., & Viner, R. M. (2012). Policy responses to multiple risk behaviours in adolescents. Journal of public health (Oxford, England), 34 Suppl 1(suppl_1), i11– 9. doi:10.1093/pubmed/fdr112
Hasebrink, U., Görzig, A., Haddon, L., Kalmus, V., & Livingstone, S. (2011). Patterns of risk and safety online: in-depth analyses from the EU Kids Online survey of 9-to 16-year-olds and their parents in 25 European countries. London, UK: London School of Economics and Political Science.Hope, A. (2007). Risk taking, boundary performance and intentional school Internet 'misuse'. Discourse, 28(1), 87-99. doi:10.1080/01596300601073663
Hasinoff, A. (2012) Sexting as media production: Rethinking social media and sexuality. New Media and Society, 10.1177/1461444812459171.
Jackson, C. a, Henderson, M., Frank, J. W., & Haw, S. J. (2012). An overview of
26
of public health (Oxford, England), 34 Suppl 1, i31–40. doi:10.1093/pubmed/fdr113
Jessor, R. (1991). Risk behavior in adolescence: A psychosocial framework for understanding and action. Journal of Adolescent Health, 12(8), 597–605. doi:10.1016/1054-139x(91)90007-k
Lenhart, A. (2009). Teens and sexting: How and why minor teens are sending sexually suggestive nude or nearly nude images via text messaging. Washington, DC: Pew Internet & American Life Project.
Lievens, E. (2012). Bullying and sexting in social networks from a legal perspective: Between enforcement and empowerment. Leuven: Katholieke Universiteit Leuven.
Livingstone, S. (2008). Taking risky opportunities in youthful content creation: teenagers' use of social networking sites for intimacy, privacy and self-expression. New Media & Society, 10(3), 393-411.
doi:10.1177/1461444808089415
Livingstone, S., & Helsper, E. J. (2010). Balancing opportunities and risks in teenagers' use of the internet: The role of online skills and internet self-efficacy. New Media & Society, 12(2), 309-329. doi:10.1177/1461444807080335
Livingstone, S., Haddon, L., & Görzig, A. (2012). (Eds.) Children, Risk and Safety on the Internet: Research and Policy Challenges in Comparative Perspective. Policy Press.
Livingstone, S., Haddon, L., Görzig, A., & Ólafsson, K. (2011). Technical report and user guide: The 2010 EU kids online survey. LSE, London: EU Kids Online. Lounsbury, K., Mitchell, K. J., & Finkelhor, D. (2011). The true prevalence of
“sexting”. Fact Sheet. Crimes against Children Research Center, University of New Hampshire. Retrieved from
http://www.unh.edu/ccrc/pdf/Sexting%20Fact%20Sheet%204_29_11.pdf Marwick, A., & boyd, d. (2011). The drama! Teen conflict, gossip, and bullying in
networked publics. Paper presented at the A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society.
Minnis, H., Rabe-Hesketh, S. & Wolkind, S. (2002). Development of a brief, clinically relevant, scale for measuring attachment disorders. International Journal of Methods in Psychiatric Research, 11, 90-98. doi:10.1002/mpr.127
Mitchell, K. J., & Wells, M. (2007). Problematic internet experiences: Primary or secondary presenting problems in persons seeking mental health care? Social Science & Medicine, 65(6), 1136-1141. doi:10.1016/j.socscimed.2007.05.015 Mitchell, K., Finkelhor, D., Jones, L. M., & Wolak, J. (2012). Prevalence and
27
National Campaign to Support Teen and Unplanned Pregnancy (2008). Sex and tech: results from a survey of teens and young adults. Washington, DC: National Campaign to Support Teen and Unplanned Pregnancy.
Orgad, S. (2007). The interrelations between ‘online’ and ‘offline’: questions, issues and implications. In R. Mansell, C. Avgerou, D. Quah & R. Silverstone (Eds.), The Oxford Handbook of Information and Communication Technologies. Oxford: Oxford University Press.
Palfrey, J., Sacco, D., boyd, d., & DeBonis, L. (2008). Enhancing child safety & online technologies: Final report of the Internet Safety Technical Task Force to the Multi-State Working Group on Social Networking of Multi-State Attorneys General of the United States. Massachusetts,MA: Harvard University, The Berkham Center for Internet & Society.
Palmer, T., & Stacey, L. (2004). Just one click: Sexual abuse of children and young people through the internet and mobile telephone technology. Ilford, UK: Barnardo's.
Peter, J., & Valkenburg, P. (2006). Adolescent´s exposure to sexually explicit online materials and recreational attitudes towards sex. Journal of Communication, 56, 639-660.
Petermann, U., Petermann, F., & Schreyer, I. (2010). The German Strengths and
Difficulties Questionnaire (SDQ): Validity of the teacher version for preschoolers.
European Journal of Psychological Assessment, 26(4), 256-262. doi:10.1027/1015-5759/a000034
Phippen, A. (2012). Sexting: An Exploration of Practices, Attitudes and Influences. NSPCC and UK Safer Internet Centre.
Ringrose, J., Gill, R., Livingstone, S., & Harvey, L. (2012). A qualitative study of children, young people and ‘sexting’: A report prepared for the NSPCC. London: NSPCC.
Rice, E., Rhoades, H., Winetrobe, H., Sanchez, M., Montoya, J., Plant, A., & Kordic, T. (2012). Sexually explicit cell phone messaging associated with sexual risk among adolescents. Pediatrics, 130(4), 667–73. doi:10.1542/peds.2012-0021
Rutter, M. (1987). Psychosocial resilience and protective mechanisms. American Journal of Orthopsychiatry, 57(3), 316–331.
doi:10.1111/j.1939-0025.1987.tb03541.x
Sacco, D. T., Argudin, R., Maguire, J., & Tallon, K. (2010). Sexting: Youth Practices and Legal Implications. Cambridge, MA: Berkman Center for Internet & Society. Salter, M., Crofts, T., & Lee, M. (2013). Beyond criminalisation and responsibilisation:
28
Schoon, I. (2006). Risk and resilience: Adaptations in changing times. Risk and resilience: Adaptations in changing times. New York, NY US: Cambridge University Press. doi:10.1017/cbo9780511490132
Sevcikova, A., Simon, L., Daneback, K., & Kvapilik, T. (2012). Bothersome exposure to online sexual content among adolescent girls. Youth & Society,
0044118X12469379–. doi:10.1177/0044118X12469379
Slater, M. D. (2003). Alienation, aggression, and sensation seeking as predictors of adolescent use of violent film, computer, and website content. Journal of Communication, 53(1), 105-121. doi: 10.1111/j.1460-2466.2003.tb03008.x Slater, M. D., Henry, K. L., Swaim, R., & Cardador, J. M. (2004). Vulnerable teens,
vulnerable times: How sensation seeking, alienation, and victimization moderate the violent media content-aggressiveness relation. Communication Research, 31(6), 642-668. doi: 10.1177/0093650204269265
Smith, R. E., Ptacek, J. T., & Smoll, F. L. (1992). Sensation seeking, stress, and adolescent injuries: a test of stress-buffering, risk-taking, and coping skills hypotheses. Journal of personality and social psychology, 62(6), 1016–24. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/1619547
Sobanski, E., Banaschewski, T., Asherson, P., Buitelaar, J., Chen, W., Franke, B., & Faraone, S. V. (2010). Emotional lability in children and adolescents with attention deficit hyperactivity disorder (ADHD): Clinical correlates and familial prevalence.
Journal of Child Psychology and Psychiatry, 51(8), 915-923. doi:10.1111/j.1469-7610.2010.02217.x
Steinberg, L., Albert, D., Cauffman, E., Banich, M., Graham, S., & Woolard, J. (2008). Age differences in sensation seeking and impulsivity as indexed by behavior and self-report: Evidence for a dual systems model. Developmental Psychology, 44(6), 1764-1778. doi:10.1037/a0012955
Stephenson, M. T., Hoyle, R. H., Palmgreen, P., & Slater, M. D. (2003). Brief measures of sensation seeking for screening and large-scale surveys. Drug and Alcohol Dependence, 72(3), 279-286. doi:10.1016/j.drugalcdep.2003.08.003
Sterne, J. A. C., White, I. R., Carlin, J. B., Spratt, M., Royston, P., Kenward, M. G., Wood, A. M., et al. (2009). Multiple imputation for missing data in
epidemiological and clinical research: potential and pitfalls. BMJ, 338(b2393), 157–160. doi:10.1136/bmj.b2393
Thabet, A., Tischler, V., & Vostanis, P. (2004). Maltreatment and coping strategies among male adolescents living in the Gaza Strip. Child Abuse & Neglect, 28(1), 77-91. doi:10.1016/j.chiabu.2002.12.002
Valkenburg, P., & Peter, J. (2008). Adolescents' identity experiments on the internet.
29
Van Nieuwenhuijzen, M., Junger, M., Velderman, M. K., Wiefferink, K. H., Paulussen, T. W. G. M., Hox, J., & Reijneveld, S. A. (2009). Clustering of
health-compromising behavior and delinquency in adolescents and adults in the Dutch population. Preventive medicine, 48(6), 572–8. doi:10.1016/j.ypmed.2009.04.008 Von Hippel, P. T. (2007). Regression With Missing Ys: an Improved Strategy for
Analyzing Multiply Imputed Data. Sociological Methodology, 37(1), 83–117. doi:10.1111/j.1467-9531.2007.00180.x
Wells, M., & Mitchell, K. J. (2008). How do high-risk youth use the internet?
Characteristics and implications for prevention. Child Maltreatment, 13(3), 227-234. doi: 10.1177/1077559507312962
Willard, N. E. (2010). Sexting and youth: Achieving a rational response. Journal of Social Sciences, 6(4), 542-562.
Willett, R., & Burn, A. (2005). 'What exactly is a paedophile?' Children talking about Internet risk. Jahrbuch Medienpädagogik, 5, 237-254.
Wolak, J., & Finkelhor, D. (2011). Sexting: a typology. New Hampshire: Crimes Against Children Center.
Wolak, J., Finkelhor, D., & Mitchell, K. (2008). Is talking online to unknown people always risky? Distinguishing online interaction styles in a national sample of youth internet users. CyberPsychology & Behavior, 11(3), 340-343.
doi:10.1089/cpb.2007.0044
Ybarra, M. L., & Mitchell, K. J. (2005). Exposure to internet pornography among children and adolescents: A national survey. CyberPsychology & Behavior, 8, 473-486.
Ybarra, M. L., & Mitchell, K. J. (2008). How risky are social networking sites? A comparison of places online where youth sexual solicitation and harassment occurs. Pediatrics, 121(2), e350-e357. doi:10.1542/peds.2007-0693
30
Footnotes
1
Missing values were less than 5% for each of the covariates and therefore not considered to cause bias in estimates. The dependent variable showed 15% missing values for risk and 11% missing values for harm. We decided against using imputation techniques to account for missing values in the dependent variables as these have shown to bias estimates when applied to dependent variables (Von Hippel, 2007). However, missing data in the dependent variable does not lead to biased estimates if sufficient covariates (as in our analysis) are included in the model (Sterne et al., 2009). Hence, we concluded that our results would be least and not substantially biased if complete case analyses were performed.
2
31 Table 1.
Multilevel Models to Predict Risk of Receiving Sexual Messages Online
Fixed: Intercept, Age, Gender (Base=Female)
Constant Age Gender
Model B SE B Exp(B) B SE B Exp(B) B SE B Exp(B)
1. Null -1.81 0.080 - - - - - - -
2. 1 + Demographics -2.08 0.092 - 0.38 0.015 1.47*** 0.24 0.047 1.27***
3. 2 + Psychological factors -2.10 0.091 - 0.36 0.016 1.44*** 0.10 0.049 1.10
4. 3 + Risk factors -2.27 0.090 - 0.19 0.018 1.20*** 0.01 0.053 1.01
Fixed (continued): Sensation Seeking, Psychological Difficulties
Sensation Seeking Psychological Difficulties
Model B SE B Exp(B) B SE B Exp(B)
3. 2 + Psychological factors 0.79 0.043 2.21*** 0.99 0.096 2.69***
4. 3 + Risk factors 0.29 0.049 1.34*** 0.43 0.106 1.54***
Fixed (continued): Online Activities, Risky Online Activities, Risky Offline Activities
Online Activities Risky Online Activities Risky Offline Activities
Model B SE B Exp(B) B SE B Exp(B) B SE B Exp(B)
4. 3 + Risk factors 0.12 0.009 1.13*** 0.36 0.018 1.44*** 0.40 0.026 1.49***
Random
Level 2 (country) VPCa Model statistics
Model variance Log-likelihood Χ2
(df)
1. Null 0.149 4.34% -6424.70 170.43(1)***
2. 1 + Demographics 0.175 5.06% -6061.33 726.75(2)***
3. 2 + Psychological factors 0.172 4.96% -5770.63 581.38(2)***
4. 3 + Risk factors 0.160 4.64% -5174.87 1191.52(3)***
Note:N=15 619 a
32 Table 2.
Multilevel Models to Predict Harm from Receiving Sexual Messages Online
Fixed: Intercept, Age, Gender (Base=Female)
Constant Age Gender
Model B SE B Exp(B) B SE B Exp(B) B SE B Exp(B)
1. Null -1.31 0.123 - - - - - - -
2. 1 + Demographics -0.89 0.138 - -0.27 0.037 0.76*** -0.93 0.116 0.40***
3. 2 + Psychological factors -0.99 0.141 - -0.25 0.038 0.78*** -0.81 0.120 0.44***
4. 3 + Risk factors -1.02 0.141 - -0.22 0.040 0.80*** -0.78 0.120 0.46***
Fixed (continued): Sensation Seeking, Psychological Difficulties
Sensation Seeking Psychological Difficulties
Model B SE B Exp(B) B SE B Exp(B)
3. 2 + Psychological factors -0.30 0.102 0.74** 1.34 0.215 3.82***
4. 3 + Risk factors -0.23 0.109 0.79* 1.38 0.222 3.99***
Fixed (continued): Online Activities, Risky Online Activities, Risky Offline Activities
Online Activities Risky Online Activities Risky Offline Activities
Model B SE B Exp(B) B SE B Exp(B) B SE B Exp(B)
4. 3 + Risk factors -0.23 0.109 0.79* 0.05 0.042 1.05 -0.09 0.056 0.91
Random
Level 2 (country) VPCa Model statistics
Model variance Log-likelihood Χ2
(df)
1. Null 0.291 8.13% -1052.77 52.81(1)***
2. 1 + Demographics 0.323 8.94% -992.67 120.20(2)***
3. 2 + Psychological factors 0.323 8.93% -972.18 40.98(2)***
4. 3 + Risk factors 0.321 8.88% -967.43 9.51(3)*
Note:N=2 036
a
